The filtered backprojection (FBP) algorithm is generally simple and efficient and is used to reconstruct images in nuclear medicine, x-ray CT and MRI. The FBP algorithm has been the workhorse in X-ray CT and nuclear medicine image reconstruction thanks to its computational efficiency. Although FBP is efficient, it undesirably produces noisy images particularly from data acquired at a low dose of X-ray.
As a general trend, the FBP algorithm is gradually being replaced by iterative algorithms despite its use for several decades. The FBP algorithm undesirably generates images with noise. Furthermore, prior art indicates that the FBP algorithm is not capable of incorporating a noise model for reducing the noise level. In this regard, iterative algorithms optionally incorporate a projection noise model and produce less noisy images than the FBP algorithm. Yet, iterative algorithms generally require intense computation.
Despite the computational requirements, iterative algorithms advantageously produce noise-resolution balanced images using maximum a posteriori (MAP). In contrast, prior art indicates that the FBP algorithm is not capable of taking advantage of MAP or prior image information.
In view of the above discussed prior art issues, a practical solution is still desired for a method and a system in reconstructing an image using the FBP algorithm to substantially reduce noise in the image without losing computational efficiency.